Overview

Dataset statistics

Number of variables17
Number of observations107360
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 MiB
Average record size in memory123.0 B

Variable types

Numeric15
Categorical2

Alerts

feature2 is highly overall correlated with feature13High correlation
feature4 is highly overall correlated with feature6 and 1 other fieldsHigh correlation
feature5 is highly overall correlated with feature8High correlation
feature6 is highly overall correlated with feature4 and 1 other fieldsHigh correlation
feature8 is highly overall correlated with feature5High correlation
feature9 is highly overall correlated with feature4 and 1 other fieldsHigh correlation
feature15 is highly overall correlated with labelHigh correlation
feature13 is highly overall correlated with feature2High correlation
label is highly overall correlated with feature15High correlation
label is highly imbalanced (65.8%)Imbalance
feature5 has unique valuesUnique
feature6 has unique valuesUnique
feature10 has unique valuesUnique
feature14 has unique valuesUnique
feature15 has unique valuesUnique
feature16 has unique valuesUnique
feature2 has 7301 (6.8%) zerosZeros
feature11 has 3901 (3.6%) zerosZeros
feature12 has 10405 (9.7%) zerosZeros

Reproduction

Analysis started2023-06-25 11:22:29.011988
Analysis finished2023-06-25 11:23:28.616234
Duration59.6 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

feature1
Real number (ℝ)

Distinct481
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.04948
Minimum0
Maximum480
Zeros153
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size209.8 KiB
2023-06-25T11:23:28.715352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1121
median236
Q3363
95-th percentile459
Maximum480
Range480
Interquartile range (IQR)242

Descriptive statistics

Standard deviation139.26754
Coefficient of variation (CV)0.58016181
Kurtosis-1.2020849
Mean240.04948
Median Absolute Deviation (MAD)123
Skewness0.018159458
Sum25771712
Variance19395.447
MonotonicityNot monotonic
2023-06-25T11:23:28.981125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
459 811
 
0.8%
288 735
 
0.7%
206 731
 
0.7%
332 727
 
0.7%
461 684
 
0.6%
85 676
 
0.6%
231 639
 
0.6%
97 637
 
0.6%
67 633
 
0.6%
250 624
 
0.6%
Other values (471) 100463
93.6%
ValueCountFrequency (%)
0 153
 
0.1%
1 243
0.2%
2 58
 
0.1%
3 393
0.4%
4 112
 
0.1%
5 468
0.4%
6 49
 
< 0.1%
7 50
 
< 0.1%
8 441
0.4%
9 525
0.5%
ValueCountFrequency (%)
480 250
0.2%
479 61
 
0.1%
478 294
0.3%
477 233
0.2%
476 298
0.3%
475 96
 
0.1%
474 377
0.4%
473 91
 
0.1%
472 57
 
0.1%
471 420
0.4%

feature2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3985656
Minimum0
Maximum13
Zeros7301
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size105.0 KiB
2023-06-25T11:23:29.209206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9209623
Coefficient of variation (CV)0.6127877
Kurtosis-1.2081832
Mean6.3985656
Median Absolute Deviation (MAD)4
Skewness-0.0026235355
Sum686950
Variance15.373945
MonotonicityNot monotonic
2023-06-25T11:23:29.441644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 11426
10.6%
12 10479
9.8%
6 9947
9.3%
7 8994
8.4%
11 8771
8.2%
2 8711
8.1%
1 7676
 
7.1%
10 7662
 
7.1%
0 7301
 
6.8%
9 6977
 
6.5%
Other values (4) 19416
18.1%
ValueCountFrequency (%)
0 7301
6.8%
1 7676
7.1%
2 8711
8.1%
3 4451
 
4.1%
4 11426
10.6%
5 6159
5.7%
6 9947
9.3%
7 8994
8.4%
8 5352
5.0%
9 6977
6.5%
ValueCountFrequency (%)
13 3454
 
3.2%
12 10479
9.8%
11 8771
8.2%
10 7662
7.1%
9 6977
6.5%
8 5352
5.0%
7 8994
8.4%
6 9947
9.3%
5 6159
5.7%
4 11426
10.6%

feature3
Real number (ℝ)

Distinct25565
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.882623
Minimum1
Maximum9875.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:29.896162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.51
Q19.94
median48.69
Q390.99
95-th percentile340.851
Maximum9875.47
Range9874.47
Interquartile range (IQR)81.05

Descriptive statistics

Standard deviation198.28937
Coefficient of variation (CV)2.0053005
Kurtosis185.78981
Mean98.882623
Median Absolute Deviation (MAD)39.22
Skewness7.8808372
Sum10616038
Variance39318.675
MonotonicityNot monotonic
2023-06-25T11:23:30.163590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11 59
 
0.1%
3.06 56
 
0.1%
1.15 55
 
0.1%
1.92 51
 
< 0.1%
3.95 50
 
< 0.1%
1.61 50
 
< 0.1%
3.54 49
 
< 0.1%
1.14 49
 
< 0.1%
1.1 48
 
< 0.1%
1.09 48
 
< 0.1%
Other values (25555) 106845
99.5%
ValueCountFrequency (%)
1 29
< 0.1%
1.01 37
< 0.1%
1.02 27
< 0.1%
1.03 30
< 0.1%
1.04 40
< 0.1%
1.05 41
< 0.1%
1.06 38
< 0.1%
1.07 41
< 0.1%
1.08 43
< 0.1%
1.09 48
< 0.1%
ValueCountFrequency (%)
9875.47 1
< 0.1%
9186.99 1
< 0.1%
8192.07 1
< 0.1%
7559.34 1
< 0.1%
6002.32 1
< 0.1%
5252.81 1
< 0.1%
4699.1 1
< 0.1%
4583.26 1
< 0.1%
4533.12 1
< 0.1%
4430.67 1
< 0.1%

feature4
Real number (ℝ)

HIGH CORRELATION 

Distinct893
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50738.075
Minimum1106
Maximum99791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:30.423362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1106
5-th percentile7302
Q128152
median46222
Q378045
95-th percentile94954
Maximum99791
Range98685
Interquartile range (IQR)49893

Descriptive statistics

Standard deviation29606.912
Coefficient of variation (CV)0.58352454
Kurtosis-1.3190359
Mean50738.075
Median Absolute Deviation (MAD)27081
Skewness0.068915724
Sum5.4472398 × 109
Variance8.7656925 × 108
MonotonicityNot monotonic
2023-06-25T11:23:30.685175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85051 534
 
0.5%
94509 487
 
0.5%
44053 477
 
0.4%
93458 369
 
0.3%
22193 367
 
0.3%
32669 303
 
0.3%
85009 297
 
0.3%
48095 297
 
0.3%
56224 294
 
0.3%
53027 293
 
0.3%
Other values (883) 103642
96.5%
ValueCountFrequency (%)
1106 199
0.2%
1431 143
0.1%
1550 216
0.2%
1570 289
0.3%
1609 141
0.1%
1701 156
0.1%
1760 141
0.1%
1880 146
0.1%
1902 156
0.1%
2081 100
 
0.1%
ValueCountFrequency (%)
99791 186
0.2%
99737 191
0.2%
99709 7
 
< 0.1%
99654 142
0.1%
99508 7
 
< 0.1%
99218 153
0.1%
99206 129
0.1%
98354 231
0.2%
98312 94
 
0.1%
98230 239
0.2%

feature5
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.240961
Minimum11.873034
Maximum76.845878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:30.940948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.873034
5-th percentile27.804471
Q133.37877
median37.492329
Q341.13894
95-th percentile45.471442
Maximum76.845878
Range64.972844
Interquartile range (IQR)7.7601697

Descriptive statistics

Standard deviation5.7189324
Coefficient of variation (CV)0.15356565
Kurtosis2.4427351
Mean37.240961
Median Absolute Deviation (MAD)3.8585391
Skewness0.38487312
Sum3998189.6
Variance32.706188
MonotonicityNot monotonic
2023-06-25T11:23:31.201902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.35434507 1
 
< 0.1%
41.69108981 1
 
< 0.1%
24.73390468 1
 
< 0.1%
33.79141045 1
 
< 0.1%
43.96771265 1
 
< 0.1%
45.91235123 1
 
< 0.1%
44.81095722 1
 
< 0.1%
25.34530655 1
 
< 0.1%
34.55221071 1
 
< 0.1%
33.89898037 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
11.87303371 1
< 0.1%
14.95912265 1
< 0.1%
15.07966291 1
< 0.1%
15.11185023 1
< 0.1%
16.02176482 1
< 0.1%
16.16453759 1
< 0.1%
16.40507518 1
< 0.1%
16.40785006 1
< 0.1%
16.42829177 1
< 0.1%
16.5092993 1
< 0.1%
ValueCountFrequency (%)
76.8458781 1
< 0.1%
75.47804188 1
< 0.1%
74.71848258 1
< 0.1%
74.59075688 1
< 0.1%
74.3803469 1
< 0.1%
74.17610596 1
< 0.1%
74.08888983 1
< 0.1%
74.06084688 1
< 0.1%
73.95149566 1
< 0.1%
73.80880658 1
< 0.1%

feature6
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.853114
Minimum-172.64929
Maximum-63.066068
Zeros0
Zeros (%)0.0%
Negative107360
Negative (%)100.0%
Memory size838.9 KiB
2023-06-25T11:23:31.520766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-172.64929
5-th percentile-121.31158
Q1-98.953357
median-87.146479
Q3-79.58829
95-th percentile-72.3855
Maximum-63.066068
Range109.58322
Interquartile range (IQR)19.365066

Descriptive statistics

Standard deviation16.35559
Coefficient of variation (CV)-0.17806245
Kurtosis0.53084475
Mean-91.853114
Median Absolute Deviation (MAD)9.3998833
Skewness-0.95396941
Sum-9861350.3
Variance267.50534
MonotonicityNot monotonic
2023-06-25T11:23:31.782961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-83.37201509 1
 
< 0.1%
-73.8957264 1
 
< 0.1%
-99.6445403 1
 
< 0.1%
-116.7091647 1
 
< 0.1%
-93.03484052 1
 
< 0.1%
-89.89625151 1
 
< 0.1%
-112.8562873 1
 
< 0.1%
-166.5222933 1
 
< 0.1%
-97.92056822 1
 
< 0.1%
-81.86970419 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
-172.6492897 1
< 0.1%
-172.0274177 1
< 0.1%
-171.9630915 1
< 0.1%
-171.1974443 1
< 0.1%
-171.0406383 1
< 0.1%
-170.6418147 1
< 0.1%
-170.5758417 1
< 0.1%
-170.5130887 1
< 0.1%
-170.4931689 1
< 0.1%
-170.3138123 1
< 0.1%
ValueCountFrequency (%)
-63.06606843 1
< 0.1%
-64.92263618 1
< 0.1%
-65.04017887 1
< 0.1%
-65.1725452 1
< 0.1%
-65.35198726 1
< 0.1%
-65.37930797 1
< 0.1%
-65.41633269 1
< 0.1%
-65.53414837 1
< 0.1%
-65.59338613 1
< 0.1%
-65.62494547 1
< 0.1%

feature7
Real number (ℝ)

Distinct739
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294032.75
Minimum194
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:32.080380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum194
5-th percentile2661
Q116719
median62009
Q3247530
95-th percentile1577385
Maximum2906700
Range2906506
Interquartile range (IQR)230811

Descriptive statistics

Standard deviation552931.69
Coefficient of variation (CV)1.8805106
Kurtosis8.5048413
Mean294032.75
Median Absolute Deviation (MAD)54143
Skewness2.8579451
Sum3.1567356 × 1010
Variance3.0573346 × 1011
MonotonicityNot monotonic
2023-06-25T11:23:32.353534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2906700 1295
 
1.2%
1595797 1067
 
1.0%
1577385 915
 
0.9%
1382480 911
 
0.8%
1312922 898
 
0.8%
2383912 852
 
0.8%
2504700 782
 
0.7%
910148 737
 
0.7%
790689 723
 
0.7%
67952 700
 
0.7%
Other values (729) 98480
91.7%
ValueCountFrequency (%)
194 101
 
0.1%
237 186
0.2%
333 294
0.3%
392 55
 
0.1%
441 148
0.1%
456 155
0.1%
614 54
 
0.1%
631 58
 
0.1%
710 186
0.2%
769 50
 
< 0.1%
ValueCountFrequency (%)
2906700 1295
1.2%
2680484 244
 
0.2%
2504700 782
0.7%
2383912 852
0.8%
1737737 359
 
0.3%
1595797 1067
1.0%
1577385 915
0.9%
1526206 365
 
0.3%
1417793 285
 
0.3%
1382480 911
0.8%

feature8
Real number (ℝ)

HIGH CORRELATION 

Distinct107044
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.239563
Minimum18.798261
Maximum71.485302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:32.620428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.798261
5-th percentile28.276562
Q133.609103
median37.560009
Q340.978207
95-th percentile44.629931
Maximum71.485302
Range52.687041
Interquartile range (IQR)7.3691045

Descriptive statistics

Standard deviation5.3832329
Coefficient of variation (CV)0.14455682
Kurtosis3.1588387
Mean37.239563
Median Absolute Deviation (MAD)3.6580575
Skewness0.4826461
Sum3998039.4
Variance28.979196
MonotonicityNot monotonic
2023-06-25T11:23:32.890249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.590491 2
 
< 0.1%
34.990082 2
 
< 0.1%
43.874085 2
 
< 0.1%
31.290395 2
 
< 0.1%
44.793168 2
 
< 0.1%
40.737036 2
 
< 0.1%
30.494648 2
 
< 0.1%
33.086985 2
 
< 0.1%
35.862467 2
 
< 0.1%
42.666296 2
 
< 0.1%
Other values (107034) 107340
> 99.9%
ValueCountFrequency (%)
18.798261 1
< 0.1%
18.823393 1
< 0.1%
18.829194 1
< 0.1%
18.83863 1
< 0.1%
18.894883 1
< 0.1%
18.912901 1
< 0.1%
18.921812 1
< 0.1%
18.943383 1
< 0.1%
18.947359 1
< 0.1%
18.953519 1
< 0.1%
ValueCountFrequency (%)
71.485302 1
< 0.1%
71.482581 1
< 0.1%
71.457397 1
< 0.1%
71.449817 1
< 0.1%
71.415706 1
< 0.1%
71.414967 1
< 0.1%
71.409252 1
< 0.1%
71.392575 1
< 0.1%
71.353192 1
< 0.1%
71.335174 1
< 0.1%

feature9
Real number (ℝ)

HIGH CORRELATION 

Distinct107210
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.860593
Minimum-169.01967
Maximum-69.13386
Zeros0
Zeros (%)0.0%
Negative107360
Negative (%)100.0%
Memory size838.9 KiB
2023-06-25T11:23:33.150622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-169.01967
5-th percentile-121.44258
Q1-98.089413
median-86.949154
Q3-80.001092
95-th percentile-73.12289
Maximum-69.13386
Range99.885809
Interquartile range (IQR)18.08832

Descriptive statistics

Standard deviation16.241312
Coefficient of variation (CV)-0.17680391
Kurtosis0.5609259
Mean-91.860593
Median Absolute Deviation (MAD)9.416795
Skewness-0.97657373
Sum-9862153.2
Variance263.78022
MonotonicityNot monotonic
2023-06-25T11:23:33.413245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-78.039653 2
 
< 0.1%
-73.531125 2
 
< 0.1%
-86.706426 2
 
< 0.1%
-122.988165 2
 
< 0.1%
-95.920992 2
 
< 0.1%
-79.823015 2
 
< 0.1%
-86.055083 2
 
< 0.1%
-86.513518 2
 
< 0.1%
-117.819622 2
 
< 0.1%
-96.171057 2
 
< 0.1%
Other values (107200) 107340
> 99.9%
ValueCountFrequency (%)
-169.019669 1
< 0.1%
-169.019569 1
< 0.1%
-168.961024 1
< 0.1%
-168.93595 1
< 0.1%
-168.898636 1
< 0.1%
-168.879543 1
< 0.1%
-168.867378 1
< 0.1%
-168.849947 1
< 0.1%
-168.830506 1
< 0.1%
-168.802586 1
< 0.1%
ValueCountFrequency (%)
-69.13386 1
< 0.1%
-69.137203 1
< 0.1%
-69.137284 1
< 0.1%
-69.141322 1
< 0.1%
-69.15635 1
< 0.1%
-69.160535 1
< 0.1%
-69.170033 1
< 0.1%
-69.173353 1
< 0.1%
-69.183253 1
< 0.1%
-69.209612 1
< 0.1%

feature10
Real number (ℝ)

UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.306559
Minimum23.447657
Maximum96.996128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:33.669008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23.447657
5-th percentile27.013771
Q134.976274
median45.014083
Q358.726041
95-th percentile81.285689
Maximum96.996128
Range73.548472
Interquartile range (IQR)23.749768

Descriptive statistics

Standard deviation16.677811
Coefficient of variation (CV)0.34524941
Kurtosis-0.10438393
Mean48.306559
Median Absolute Deviation (MAD)11.512617
Skewness0.77088697
Sum5186192.1
Variance278.14937
MonotonicityNot monotonic
2023-06-25T11:23:33.938499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.52929005 1
 
< 0.1%
37.50271487 1
 
< 0.1%
27.01024837 1
 
< 0.1%
47.09895409 1
 
< 0.1%
36.91729101 1
 
< 0.1%
43.84812883 1
 
< 0.1%
76.37331677 1
 
< 0.1%
52.97824142 1
 
< 0.1%
38.03501023 1
 
< 0.1%
46.00613194 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
23.44765672 1
< 0.1%
23.76904238 1
< 0.1%
23.86962919 1
< 0.1%
23.91537752 1
< 0.1%
23.94502762 1
< 0.1%
23.96328287 1
< 0.1%
23.99754265 1
< 0.1%
24.02708902 1
< 0.1%
24.03170502 1
< 0.1%
24.08620017 1
< 0.1%
ValueCountFrequency (%)
96.99612844 1
< 0.1%
96.89987523 1
< 0.1%
96.79043027 1
< 0.1%
96.78070484 1
< 0.1%
96.68846318 1
< 0.1%
96.68206388 1
< 0.1%
96.60726724 1
< 0.1%
96.56237445 1
< 0.1%
96.52626482 1
< 0.1%
96.50563664 1
< 0.1%

feature11
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.706501
Minimum0
Maximum23
Zeros3901
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:34.403071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median13
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0599755
Coefficient of variation (CV)0.55561914
Kurtosis-1.1663527
Mean12.706501
Median Absolute Deviation (MAD)6
Skewness-0.24002521
Sum1364170
Variance49.843254
MonotonicityNot monotonic
2023-06-25T11:23:34.631300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 6726
 
6.3%
23 6688
 
6.2%
13 5150
 
4.8%
20 5147
 
4.8%
12 5079
 
4.7%
19 5062
 
4.7%
17 5050
 
4.7%
16 5020
 
4.7%
15 5009
 
4.7%
21 5007
 
4.7%
Other values (14) 53422
49.8%
ValueCountFrequency (%)
0 3901
3.6%
1 3975
3.7%
2 4114
3.8%
3 4038
3.8%
4 3359
3.1%
5 3499
3.3%
6 3518
3.3%
7 3494
3.3%
8 3404
3.2%
9 3419
3.2%
ValueCountFrequency (%)
23 6688
6.2%
22 6726
6.3%
21 5007
4.7%
20 5147
4.8%
19 5062
4.7%
18 4984
4.6%
17 5050
4.7%
16 5020
4.7%
15 5009
4.7%
14 4960
4.6%

feature12
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4800857
Minimum0
Maximum6
Zeros10405
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:34.844764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9910647
Coefficient of variation (CV)0.57213093
Kurtosis-1.1886886
Mean3.4800857
Median Absolute Deviation (MAD)2
Skewness-0.29624589
Sum373622
Variance3.9643386
MonotonicityNot monotonic
2023-06-25T11:23:35.067955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 22031
20.5%
5 19545
18.2%
4 16576
15.4%
2 14332
13.3%
1 12335
11.5%
3 12136
11.3%
0 10405
9.7%
ValueCountFrequency (%)
0 10405
9.7%
1 12335
11.5%
2 14332
13.3%
3 12136
11.3%
4 16576
15.4%
5 19545
18.2%
6 22031
20.5%
ValueCountFrequency (%)
6 22031
20.5%
5 19545
18.2%
4 16576
15.4%
3 12136
11.3%
2 14332
13.3%
1 12335
11.5%
0 10405
9.7%

feature13
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size838.9 KiB
1
55451 
2
49874 
3
 
2035

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107360
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

Length

2023-06-25T11:23:35.294060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T11:23:35.552845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107360
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 107360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55451
51.6%
2 49874
46.5%
3 2035
 
1.9%

feature14
Real number (ℝ)

UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4967069
Minimum-1.0035501
Maximum16.812962
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size838.9 KiB
2023-06-25T11:23:35.766315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.0035501
5-th percentile4.198523
Q16.1518838
median7.4997775
Q38.844669
95-th percentile10.776936
Maximum16.812962
Range17.816512
Interquartile range (IQR)2.6927852

Descriptive statistics

Standard deviation1.9971087
Coefficient of variation (CV)0.26639813
Kurtosis-0.00022876173
Mean7.4967069
Median Absolute Deviation (MAD)1.3464026
Skewness-0.014684824
Sum804846.46
Variance3.9884432
MonotonicityNot monotonic
2023-06-25T11:23:36.030047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.406360481 1
 
< 0.1%
6.760933755 1
 
< 0.1%
5.089393105 1
 
< 0.1%
9.145780571 1
 
< 0.1%
10.90147798 1
 
< 0.1%
7.307428649 1
 
< 0.1%
10.19816652 1
 
< 0.1%
6.121272138 1
 
< 0.1%
9.053955413 1
 
< 0.1%
7.833367766 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
-1.003550085 1
< 0.1%
-0.9290672109 1
< 0.1%
-0.847267109 1
< 0.1%
-0.6487660799 1
< 0.1%
-0.6311311885 1
< 0.1%
-0.5234251099 1
< 0.1%
-0.4366576635 1
< 0.1%
-0.4186388095 1
< 0.1%
-0.4177912709 1
< 0.1%
-0.4026674936 1
< 0.1%
ValueCountFrequency (%)
16.81296174 1
< 0.1%
15.72474357 1
< 0.1%
15.71535894 1
< 0.1%
15.5996583 1
< 0.1%
15.26951288 1
< 0.1%
15.26006981 1
< 0.1%
15.21305893 1
< 0.1%
15.16249969 1
< 0.1%
15.03182214 1
< 0.1%
15.0045217 1
< 0.1%

feature15
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53190045
Minimum-0.33534564
Maximum1.7033392
Zeros0
Zeros (%)0.0%
Negative621
Negative (%)0.6%
Memory size838.9 KiB
2023-06-25T11:23:36.338184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.33534564
5-th percentile0.17528908
Q10.37558729
median0.51713089
Q30.66708538
95-th percentile0.95005698
Maximum1.7033392
Range2.0386849
Interquartile range (IQR)0.29149809

Descriptive statistics

Standard deviation0.23486759
Coefficient of variation (CV)0.44156306
Kurtosis0.77097238
Mean0.53190045
Median Absolute Deviation (MAD)0.14568023
Skewness0.50395125
Sum57104.832
Variance0.055162784
MonotonicityNot monotonic
2023-06-25T11:23:36.600478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2848339943 1
 
< 0.1%
0.5083875308 1
 
< 0.1%
0.4898885857 1
 
< 0.1%
0.2077378266 1
 
< 0.1%
0.4423484612 1
 
< 0.1%
0.6953744124 1
 
< 0.1%
0.9457802722 1
 
< 0.1%
0.6993925361 1
 
< 0.1%
0.166495547 1
 
< 0.1%
0.3710581304 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
-0.3353456445 1
< 0.1%
-0.3213764401 1
< 0.1%
-0.3058587155 1
< 0.1%
-0.2967268268 1
< 0.1%
-0.2933620709 1
< 0.1%
-0.2920209356 1
< 0.1%
-0.2884168614 1
< 0.1%
-0.2832487307 1
< 0.1%
-0.2805849503 1
< 0.1%
-0.2778321205 1
< 0.1%
ValueCountFrequency (%)
1.703339221 1
< 0.1%
1.666962389 1
< 0.1%
1.652025332 1
< 0.1%
1.651679277 1
< 0.1%
1.647956543 1
< 0.1%
1.647864592 1
< 0.1%
1.627449452 1
< 0.1%
1.625455378 1
< 0.1%
1.619805196 1
< 0.1%
1.615795848 1
< 0.1%

feature16
Real number (ℝ)

UNIQUE 

Distinct107360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.232903
Minimum44.152711
Maximum88.834367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size838.9 KiB
2023-06-25T11:23:36.917424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum44.152711
5-th percentile57.451665
Q162.759422
median66.313658
Q369.793228
95-th percentile74.733936
Maximum88.834367
Range44.681657
Interquartile range (IQR)7.0338056

Descriptive statistics

Standard deviation5.265216
Coefficient of variation (CV)0.079495474
Kurtosis0.088114034
Mean66.232903
Median Absolute Deviation (MAD)3.5136515
Skewness-0.093497159
Sum7110764.4
Variance27.7225
MonotonicityNot monotonic
2023-06-25T11:23:37.172470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.0974369 1
 
< 0.1%
65.99569862 1
 
< 0.1%
72.39272219 1
 
< 0.1%
67.55977809 1
 
< 0.1%
56.4822707 1
 
< 0.1%
70.01956442 1
 
< 0.1%
60.83990727 1
 
< 0.1%
68.83523332 1
 
< 0.1%
72.23198183 1
 
< 0.1%
70.87719617 1
 
< 0.1%
Other values (107350) 107350
> 99.9%
ValueCountFrequency (%)
44.15271061 1
< 0.1%
44.21972587 1
< 0.1%
44.4247799 1
< 0.1%
44.59322547 1
< 0.1%
44.68902034 1
< 0.1%
44.70530797 1
< 0.1%
44.88639665 1
< 0.1%
45.28013744 1
< 0.1%
45.28030756 1
< 0.1%
45.2890868 1
< 0.1%
ValueCountFrequency (%)
88.8343673 1
< 0.1%
88.59156936 1
< 0.1%
87.48454737 1
< 0.1%
87.43937296 1
< 0.1%
87.28445786 1
< 0.1%
86.9780624 1
< 0.1%
86.78564545 1
< 0.1%
86.53818716 1
< 0.1%
86.23151857 1
< 0.1%
86.02992936 1
< 0.1%

label
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size838.9 KiB
0
100515 
1
 
6845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107360
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Length

2023-06-25T11:23:37.551031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T11:23:37.782470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107360
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 107360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100515
93.6%
1 6845
 
6.4%

Interactions

2023-06-25T11:23:23.809098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:33.674828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:37.257092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:40.719508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:44.473852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:47.937918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:51.413889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:54.968395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:58.630353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:02.116295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:05.645385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:09.279377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:12.814994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:16.332259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:19.949728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:24.024270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:33.890461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:37.482261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:40.967479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:44.692855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:48.159253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:51.639932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:55.187676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:58.875315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:02.338934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:05.868689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:09.495209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:13.032359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:16.587742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:20.186590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:24.258485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:34.112148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:37.699422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:41.194266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:44.915661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:48.390351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:51.866551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:55.408440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:59.097376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:02.682608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:06.127282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:09.721234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:13.263021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:16.875383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:20.420206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:24.476299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:34.374732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:37.916705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:41.413433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:45.152047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:48.610420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:52.084979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:55.678791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:59.316468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:02.893701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:06.484534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:09.938546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:13.485696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:17.130908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:20.649115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:24.695040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:34.598963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:38.134613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:41.632547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:45.384222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:48.851852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:52.310708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:55.986773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:59.586851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:03.108034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:06.707690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:10.189123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:13.708094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:17.367262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:20.903727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:24.932496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:34.915815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:38.392282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:41.860862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:45.694275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:49.108548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:52.541747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:56.246620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:59.820229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:03.343893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:06.946489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:10.432498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:13.967408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:17.601700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:21.154786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:25.148633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:35.234679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:38.668674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:42.080333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:45.912631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:49.352142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:52.763838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:56.468230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:00.071025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:03.556451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:07.175737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:10.666905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:14.220640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:17.825202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:21.709367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:25.372674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:35.463500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:38.893383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:42.313255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:46.138047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:49.578882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:53.109522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:56.690572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:00.296342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:03.775459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:07.422722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:10.895391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:14.448455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:18.055983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:21.945657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:25.624753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:35.678200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:39.122108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:42.531838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:46.357100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:49.804492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:53.329278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:56.911614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:00.512425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:04.012689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:07.646172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:11.114650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:14.676654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:18.279916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:22.169175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:25.839295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:35.888557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:39.347229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:42.811500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:46.572582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:50.023479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:53.560685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:57.124393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:00.727058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:04.248114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:07.869270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:11.336036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:14.903734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:18.501597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:22.407031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:26.103746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:36.114995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:39.585129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:43.175979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:46.809779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:50.257992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:53.795014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:57.364011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:00.966782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:04.491010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:08.111201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:11.563366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:15.142476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:18.736503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:22.644953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:26.324937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:36.340254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:39.801784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:43.391567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:47.028327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:50.472819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:54.005863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:57.604892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:01.182796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:04.709571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:08.331861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:11.772313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:15.363124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:18.954873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:22.863369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:26.544020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:36.576034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:40.023494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:43.660005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:47.261509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:50.698470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:54.255618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:57.931735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:01.408317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:04.960508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:08.578396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:12.112673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:15.633578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:19.258306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:23.092067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:26.766017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:36.807699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:40.250546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:44.015428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:47.495214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:50.928711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:54.517284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:58.176654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:01.632439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:05.197602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:08.812932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:12.343641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:15.874870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:19.488092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:23.329775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:27.061990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:37.042193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:40.481123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:44.259187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:47.725233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:51.170729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:54.756975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:22:58.415945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:01.865112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:05.429601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:09.056779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:12.570715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:16.110651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:19.733566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T11:23:23.589609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-25T11:23:37.954216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature14feature15feature16feature13label
feature11.000-0.0020.000-0.0290.0250.0360.0250.0330.0380.004-0.0030.0010.003-0.0040.0000.0100.005
feature2-0.0021.000-0.0150.001-0.0000.0010.001-0.0000.0000.0020.258-0.0360.0020.020-0.0160.6230.186
feature30.000-0.0151.0000.003-0.005-0.0030.000-0.004-0.0030.017-0.097-0.0140.0030.109-0.0720.0100.335
feature4-0.0290.0010.0031.000-0.207-0.9640.119-0.230-0.9760.0160.001-0.002-0.000-0.0010.0020.0000.021
feature50.025-0.000-0.005-0.2071.0000.208-0.1750.9170.2130.034-0.0080.0020.001-0.004-0.0010.0050.000
feature60.0360.001-0.003-0.9640.2081.000-0.0990.2320.983-0.0220.0020.0010.0000.001-0.0020.0020.007
feature70.0250.0010.0000.119-0.175-0.0991.000-0.189-0.099-0.012-0.0000.005-0.002-0.0000.0050.0000.003
feature80.033-0.000-0.004-0.2300.9170.232-0.1891.0000.2370.036-0.0090.0010.000-0.003-0.0010.0000.012
feature90.0380.000-0.003-0.9760.2130.983-0.0990.2371.000-0.0220.0020.002-0.000-0.000-0.0020.0000.000
feature100.0040.0020.0170.0160.034-0.022-0.0120.036-0.0221.000-0.1570.0060.0000.031-0.0200.0070.086
feature11-0.0030.258-0.0970.001-0.0080.002-0.000-0.0090.002-0.1571.000-0.005-0.0010.021-0.0140.4570.293
feature120.001-0.036-0.014-0.0020.0020.0010.0050.0010.0020.006-0.0051.0000.002-0.0300.0190.2530.070
feature140.0030.0020.003-0.0000.0010.000-0.0020.000-0.0000.000-0.0010.0021.0000.006-0.0030.0080.002
feature15-0.0040.0200.109-0.001-0.0040.001-0.000-0.003-0.0000.0310.021-0.0300.0061.000-0.1050.0220.707
feature160.000-0.016-0.0720.002-0.001-0.0020.005-0.001-0.002-0.020-0.0140.019-0.003-0.1051.0000.0080.370
feature130.0100.6230.0100.0000.0050.0020.0000.0000.0000.0070.4570.2530.0080.0220.0081.0000.042
label0.0050.1860.3350.0210.0000.0070.0030.0120.0000.0860.2930.0700.0020.7070.3700.0421.000

Missing values

2023-06-25T11:23:27.429187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-25T11:23:28.077617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13feature14feature15feature16label
03370138.714412139.354345-83.37201576934141.359975-80.58730678.52929010116.4063600.28483467.0974370
1365126.516644235.920414-98.4805881428339.439880-96.93880945.23446118226.8004470.77924566.8929960
218463.344207136.155589-88.4120502965537.220467-88.39303343.06362022228.3424280.60369966.2885860
33904180.173006731.949179-82.26579830230933.430411-84.92053926.0934870318.6974870.47225463.8349910
4365108.926644240.881803-99.9837151428338.770383-95.87735644.83144316526.5448430.42982462.7463480
58513.507604931.025928-98.5886684711933.391893-98.01053747.72507517027.7488730.79750666.3227090
6175432.124804442.866744-80.3918047958042.982443-82.51100437.0256606217.1590920.55650062.5456000
7205132.927443236.650170-96.403826973034.616556-96.53534426.67538820239.8895890.38786277.0467590
8234139.979514838.135865-122.62426997384937.949816-120.81452748.29429823225.4852920.61711763.5607380
935114.283281026.052917-82.04572987824327.811801-81.08698946.76919114326.4759440.88396267.6207120
feature1feature2feature3feature4feature5feature6feature7feature8feature9feature10feature11feature12feature13feature14feature15feature16label
10735029517.709005732.118964-122.233633238391233.628888-117.30488151.22640856210.4893100.82784164.4034580
1073513884196.636621042.063282-97.23524237021239.020253-93.79979634.5456592114.4446410.33231261.9936620
107352394717.753858336.546145-84.9923402382535.362495-84.52358039.72298215325.2028820.84295367.1698420
1073531411269.555636447.089296-94.435613541646.559485-94.65271424.74195813615.8296410.50362163.7544540
107354302914.178910337.080829-115.655228141779336.023262-115.34131932.72884513611.8273570.63199062.6890520
1073552881189.369266331.949042-119.5728556294632.972526-118.24951926.57382619617.5481090.44848368.7997750
107356122066.187411535.039883-98.24538941357436.290567-95.09093543.87808117428.5740510.62868167.9817610
107357340722.846337639.079630-93.6224697082839.334027-89.70749537.77024218028.8028720.48550360.9684250
10735853118.761740245.598813-74.12488917562440.403708-77.37012438.85413614317.6180880.37522164.3686030
107359135299.957550133.812630-95.2971996067734.117342-94.07521435.7550491219.3843630.55977368.8719160